Limits of shared LLMs: Why owning your AI in the cloud is the smart move

Large language models (LLMs) have, without a doubt, shaken up the world of natural language processing. From those surprisingly helpful chatbots to the near-instant translators and text interpreters, LLMs are a huge leap forward.

Initially, the idea of just plugging into a shared LLM offered a tempting shortcut — instant access to powerful AI without the headache of building and maintaining it all by yourself. It’s much like renting a hotel room or a serviced apartment versus renting an entire house. As these language powerhouses began to be used for increasingly critical tasks, they were exposed to growing volumes of business-specific contextual queries and data — unlike their original training on general internet content. This led to a rising need for customised or fine-tuned models to deliver higher performance and improved results. The cracks in the shared foundation thus began to show, making a strong case for bringing your AI in-house, albeit in the cloud.

The real-world headaches of shared LLMs: Shared pay-as-you-go models aren’t always cheap
The initial convenience of shared LLMs is undeniable. Someone else does the heavy lifting of the infrastructure — handling high-end GPUs and data centre complexities such as high-speed networks— while you just tap into the magic. But this shared nature comes with a set of limitations that can really sting as your needs grow:

• The latency lottery: Imagine trying to get through to customer service during peak hours, like a hotel’s restaurant at peak hours. The endless hold music at that time and the eventual, sometimes sluggish, response are very frustrating and disregard the value of your time. Shared LLMs face similar traffic jams. When everyone hits the same set of resources, response time becomes painfully slow. That once-lightning-fast chatbot suddenly leaves your customers hanging, leading to drop-offs and dissatisfaction.
• The meter’s always running: Just like that hotel agreement with limits on guests or on the expensive mini-bar, shared LLM vendors often impose rate limits or throttling or expensive add-on services. If your application suddenly experiences a surge in popularity or requires frequent interactions with the model, you quickly find yourself hitting those walls, forcing you to either scale back your ambitions or face hefty surge charges.
• The control conundrum: When you rely on a third party, you’re essentially playing by someone else’s rules. You have limited say in how the infrastructure is set up, making it tough to really optimise performance for your specific use case or to implement the exact data privacy and security measures you need. It’s like trying to customise that hotel room that you have rented – there’s only so much you can do.

Building your own AI powerhouse: Scalability and security in the cloud
The cloud has stood the test of time. Enterprises of all sizes and across industries leverage the cloud in some way or the other. When it can host entire virtual data centres, it can certainly host today’s LLMs. However, moving to your own LLM setup in the cloud sounds like a big leap and prevents you from leveraging it to your advantage. As a result, you’re losing out the control and scalability you need for business-critical AI applications. With LLMs on the cloud, you get:

• Performance on demand: When you have your own dedicated resources, you can optimise their performance as per your specific needs. This means faster response times, more reliable service, and a much better experience for your users.
• Data lockdown: Hosting your LLM in your own cloud environment gives you the peace of mind of knowing that your sensitive data and proprietary AI models are protected by your own stringent security protocols.
• Smart spending, long-term gains: Although there’s an upfront investment, setting up an optimised, private LLM is cost-effective in the long run, especially for high-volume applications. You’re not paying per query to a third party, you’re renting your own AI infrastructure.

The blueprint for AI ownership: Deploying private LLMs in the cloud
Owing a cloud-based LLM may seem like biting more than you can chew. However, with a thoughtfully crafted strategy, getting your private AI powerhouse in the cloud can be surprisingly less complicated than you imagined. Here are some key considerations towards that goal:

• Pick your cloud partner wisely: Choose a cloud provider that not only offers the raw computing power you need but also has a strong ecosystem of AI and machine learning services. Think of them as your partner in building your AI future.
• Think inside the box (Container): Technologies like Docker and Kubernetes make it much easier to manage and scale your LLM deployments, allowing you to adapt quickly to changing demands.
• Scale smart, not hard: Implement autoscaling so your resources automatically adjust based on traffic. This ensures you always have the power you need without overpaying for idle capacity.
• Save on initial investment: Pick a cloud provider that offers a competitive price in the region and can meet your specialised demands as a managed service provider.

Operating your private AI: A step-by-step guide
So, you’re convinced that taking control of your AI journey is the smart move. But what does that actually look like in practice? Getting your private LLM up and running in the cloud involves a few key steps, each designed to ensure your AI infrastructure is robust, efficient, and perfectly tailored to your business needs. Think of it as following a proven recipe to prepare your very own, customised dish. The steps for operating your private LLM in the cloud are:

• Equip your AI Infrastructure: Choose the right high-performance hardware, like powerful GPUs, to give your LLM the processing muscle it needs.
• Bring your model to life: Deploy your chosen LLM onto your cloud infrastructure, using containerisation to keep things organised and scalable.
• Open the lines of communication: Create a secure and reliable API that allows your applications to easily interact with your LLM.
• Keep a close eye and tune as needed: Continuously monitor your LLM’s performance and adjust your infrastructure and model configuration to ensure it’s running smoothly and efficiently.

Beyond automation: Private LLM-powered agentic AI

The future of AI isn’t just about automating simple tasks; it’s about creating intelligent agents that can think and act on their own based on varied use cases. And, this is where the real synergy between agentic AI and private LLMs emerges:

• Take on the tough stuff: Imagine agentic AI that can not only answer customer questions but also proactively resolve issues, manage your complex supply chain, or even analyse market trends and suggest strategic moves — all autonomously. Private LLMs have the potential to provide sophisticated language understanding that makes this level of agentic capability possible for your business use case.
• Turn data into wisdom: AI agents can sift mountains of data and extract valuable insights that can guide your business decisions, giving you a significant edge in the market. Otherwise, your AI agent is just like any other AI bot in a similar business environment with no competitive advantage.
• Make it personal, in a smart way: By understanding the nuances of language, agentic AI, powered by your LLM, can create personalised experiences for your users, offering tailored advice, anticipating their needs, and making interactions feel much more intuitive and human.

The real cost and return: Looking at the long-term value

When moving to private LLMs in the cloud, it’s important to look at the big picture, which is the TCO. This includes not just the initial infrastructure costs but also the ongoing maintenance, support, and remuneration of the personnel you’ll need to manage it. While it may seem like a big, upfront investment, the long-term benefits of improved performance, enhanced security, and greater control often make it a more sensible financial decision, especially for leveraging AI at scale.

The future is private: Taking control of your AI journey

While shared LLMs did offer a convenient starting point, their limitations have become increasingly restrictive as our growing reliance on sophisticated language AI. Dedicated or private LLM deployments in the cloud offer a smooth path forward. They provide the performance, security, and control necessary to harness the potential of agentic AI and build innovative, transformative applications. The time has come to move beyond simply renting AI solutions like a hotel room; instead, start building your own AI powerhouse in the cloud like owning a house.

To know more : Tata Communications Vayu AI Cloud platform powered by NVIDIA GPUs | Tata Communications